The instant invention relates to high fidelity, high data rate wireless communication systems.
The communication radio frequency spectrum is a valuable commodity. Part of the bandwidth is licensed and regulated by the government for its own use and that of commercial wireless communication systems, such as the cellular communication standard Universal Mobile Telecommunications System (UMTS). The remaining bandwidth is unlicensed and populated mostly with low cost wireless communication systems, including the wireless local area network (WLAN) standard IEEE 802.11. The increasing demand for bandwidth-intensive applications over wireless links requires maximizing the data rate in the available bandwidth. The capacity of a communication channel, also known as the Shannon capacity, is the upper limit on the achievable data rate through the channel in the presence of noise, and is a function of the available bandwidth and the available transmit power.
The achieved data rates are limited by errors in data transmission related to low signal levels at the receiver, relative to noise and interference. The low strength signal is difficult to detect reliably, leading to loss in fidelity. Because of the resulting increased error rate, re-transmissions are required and this results in a lowered effective data rate. The noise is typically due to thermal and other electronic noise in the receiver, while the interference is due to corrupting radio signals received from sources known or unknown, such as additional users or adversaries using jammers intent on disrupting communication. The interference is more acute in the unlicensed bandwidth because of the lack of government supervision.
The signal to noise strength ratio (SNR) at the receiver provides a measure of the fidelity for a wireless communication system. SNR is the ratio of the power of the desired signal to the power in the background noise as measured at the receiver. Higher SNR correlates with higher fidelity data transmission, and fewer requested re-transmissions. A bit error rate (BER) of 10−2 or lower renders communication feasible. Lower BER values result in improved fidelity. As an example, a system with a BER value of 10−6 provides higher fidelity than one with a BER of 10−4. The SNRs that achieve these BERs provide high fidelity wireless communication. Interference signals, when present, lower the signal to (interference-plus) noise ratio, thus increasing the BER and degrading the performance.
When some defining characteristics of the interferers are known, such as the statistics of the signals they transmit and estimates of the transformations that those signals undergo as they traverse the wireless channels, multi user detection (MUD) techniques can be used to suppress the interference and increase the SNR. See e.g., Sergio Verdu, “Multiuser Detection,” Cambridge University Press, 1998. These techniques, however, are computationally complex, assume a degree of knowledge about the interferers, and are specialized to the wireless communication technology for which they are developed.
Where, before transmitting any user data signals, the communication system transmits a training sequence, i.e. a pattern of signals known a priori by both the transmitter and the receiver, the training sequence can be exploited at the receiver to mitigate interference in the wireless channel through the use of adaptive gradient search algorithms like the Lease Mean Squares (LMS) and the Recursive Least Squares (RLS) techniques, or sub-space projection techniques. See e.g., Simon Haykin, “Adaptive Filter Theory,” Fourth Edition, Prentice Hall, 2002, Chapter 5 & Chapter 9; L. Zhao, M. G. Amin, and A. R. Lindsey, “GPS Anti-jam via Subspace Projection: A Performance Analysis for FM Interference in the C/A Code,” Digital Signal Processing, Volume 12, Issues 2-3, 2002. The presence of the training sequence, however, decreases the overall throughput of the system. Blind techniques can be used when training sequences are not available, but the blind techniques are in general computationally expensive, non-modular, and have poor overall performance. See e.g., Simon Haykin, “Adaptive Filter Theory,” Fourth Edition, Prentice Hall, 2002, Chapter 16.
If the directionalities of the desired users and interferers are known, or can be inferred, antenna-based techniques can provide interference mitigation. For instance, the radiation pattern of a multiple antenna array at the receiver can be designed to locate nulls in the directions of the interference signals (null steering), and peaks in the directions of the desired signals (beam steering). Conversely, the transmitter antenna array radiation patterns can be modified to have the peaks coincide with the desired receiver antennas and the nulls with those of the users with whom it is desired not to interfere.
Where multiple copies of the same transmitted signal are available at the receiver, the receiver can use these copies to increase reliability of reception. In such “diversity” systems, copies of the transmitted signal may be obtained at multiple time instances (time diversity), across multiple receive antennas (space diversity), or a combination of the two (space-time diversity).
Interference cancellation in diversity systems are subjects of existing United States patents. For instance, U.S. Pat. Nos. 5,268,927 and 5,596,600 use multiple signals received at successive times as input to a digital Adaptive Transversal Filter to cancel out the interference. For effective performance, however, the interference needs to be correlated in time. Where the interference signal is random, without significant correlation between the signals received at successive times, the performance degrades.
Multiple antenna interference cancellation techniques depend on specialized assumptions, and suffer from a high computational burden. For instance, U.S. Pat. No. 5,694,416 assumes knowledge of the directionality of the desired transmitter. Estimation of the directionality increases the computational burden. Interference mitigation is achieved at the receiver by maximizing the ratio of the desired signal power to the interference power. The technique, however, is unable to distinguish between transmissions from co-located transmitter antennas, as in conventional multi-input multi-output (MIMO) systems. Integrated adaptive spatial-temporal techniques, using signals received at multiple successive time instances and across multiple antennas, as in U.S. Pat. No. 6,115,409, are too complex for high data rate systems, since the signals from the successive time instances have to be buffered and many more filter coefficients have to be computed compared to where only multiple antenna signals are used. The receiver of U.S. Pat. No. 6,115,409 has knowledge of the desired (satellite) transmitter and its pseudorandom noise (PN) code, which it uses to demodulate the signal received from the satellite, a process that results in a partial decorrelation of the interference signals received with the satellite signal. The integrated adaptive spatial-temporal filter, which employs ongoing concurrent feedback for optimizing the filter coefficients for maximizing the signal-to-interference-plus-noise ratio (SINR), provides significant filtering of interference signals beyond that achieved in the subsequent receiver processing by the inherent partial decorrelation of the signals.
Wireless communication systems employing spatial multiplexing with multiple antennas at the transmitter and the receiver have yielded among the highest communication data rates in a given frequency bandwidth. Spatial multiplexing involves splitting the source data into multiple data streams and transmitting them simultaneously on separate antennas. Although the antennas operate at the same radio frequency, the data streams undergo independent fading as they travel their individual paths to the receiver antennas. Where the receiver antennas are spaced physically apart from each other by at least one-half of the transmitted radio wavelength, the data streams arrive spatially uncorrelated. They can be distinguished from each other through an estimation of their channel transformations by using algorithms known in the art, e.g., V-BLAST (P. W. Wolniansky, G. J. Foschini, G. D. Golden, and R. A. Valenzuela, “V-BLAST: An Architecture for Realizing Very High Data Rates over the Rich-scattering Wireless Channel,” Proc. ISSSE, Sept 1998, pp. 295-300; G. D. Golden, G. J. Foschini, R. A. Valenzuela, and P. W. Wolniansky, “Detection Algorithm and Initial Laboratory Results using V-BLAST Space-time Communication Architecture,” Electronics Letters, vol. 35, No. 1, Jan. 7, 1999, pp. 14-15). By transmitting and receiving multiple data streams simultaneously on separate antennas operating at the same radio frequency, higher data rates are achieved compared to where a single data stream is communicated.
In spatially multiplexed MIMO systems, if the number of receiver antennas equal or exceed the number of the transmitted data streams and if the independent fading channels corresponding to these data streams are known for transmissions with sufficiently high SNR (for high fidelity), then all of the transmitted data streams can be detected and a spectral efficiency approximately proportional to the number of transmitted data streams achieved. These prior art MIMO systems, however, are largely unable to cope with interference from co-channel jamming signals, hostile or otherwise. The lowered SNR causes the error rates to degrade and the data rates to suffer unacceptably due to the requested re-transmissions.
An example of co-channel interference mitigation in MIMO systems is provided by a MIMO-OFDM (OFDM=orthogonal frequency-division multiplexing) system using a space-time equalizer for filtering the interfering signals, as described in T. Tang and R. W. Heath, Jr., “A Direct Training-based Method for Joint Space-Time Interference Cancellation in MIMO-OFDM Systems,” Proc. IEEE GLOBECOM 2004. Space-time equalization uses two-dimensional filters in the spatial and time domains for compensation of distortions in band-limited channels. The performance of this technique, however, depends on calculating the filter coefficients through a training sequence, a facility unavailable in hostile environments. This approach also does not readily adapt to other wireless technologies.
For MIMO type high data rate wireless communication systems, a low complexity and modular digital interference suppression scheme that acts on the instantaneous signals without requiring a training sequence or concurrent feedback is desired. Modularity denotes the ability to apply the technique to a wide range of wireless communication systems. Thus, communication systems already deployed may be upgraded for interference protection, and the technique can be integrated with the Software Defined Radio (SDR) technology, which implements multiple wireless communication technologies on a common hardware platform. See e.g. Joseph Mitola, III, “Software Radio Architecture: Object-Oriented Approaches to Wireless Systems Engineering,” John Wiley & Sons, Inc. 2000.
The invention herein provides the hitherto unavailable, but highly desirable, modular, low complexity, multiple-antenna based mitigation system and method that is effective against hostile co-channel interference in a variety of high data rate wireless communication systems (including MIMO-OFDM and MIMO-CDMA), and that does not require user knowledge, concurrent feedback, or a training sequence.